Learning from text-based close call data

A key feature of big data is the variety of data sources that are available; which include not just numerical data but also image or video data or even free text. The GB railways collects a large volume of free text data daily from railway workers describing close call hazard reports: instances where an accident could have – but did not – occur. These close call reports contain valuable safety information which could be useful in managing safety on the railway, but which can be lost in the very large volume of data – much larger than is viable for a human analyst to read. This paper describes the application of rudimentary natural language processing (NLP) techniques to uncover safety information from close calls. The analysis has proven that basic information extraction is possible using the rudimentary techniques, but has also identified some limitations that arise using only basic techniques. Using these findings further research in this area intends to look at how the techniques that have been proven to date can be improved with the use of more advanced NLP techniques coupled with machine-learning

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease